noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of unreproducible, random background noise to capture true, functionally meaningful biological signals is still a challenge. Intrinsic sequencing variability that introduces low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR can be applied to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimising technical noise on plant and animal datasets, across various sequencing assays: coding, non-coding RNAs and their interactions, at bulk and single cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches. TeaserNoise removal from sequencing quantification improves the convergence of downstream tools and robustness of conclusions.